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| <div align="center"> | |
| <p> | |
| <a align="center" href="https://ultralytics.com/yolov5" target="_blank"> | |
| <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png"></a> | |
| </p> | |
| [英文](README.md)|[简体中文](README.zh-CN.md)<br> | |
| <div> | |
| <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a> | |
| <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a> | |
| <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a> | |
| <br> | |
| <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> | |
| <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | |
| <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | |
| </div> | |
| <br> | |
| YOLOv5 🚀 是世界上最受欢迎的视觉 AI，代表<a href="https://ultralytics.com"> Ultralytics </a>对未来视觉 AI 方法的开源研究，结合在数千小时的研究和开发中积累的经验教训和最佳实践。 | |
| 我们希望这里的资源能帮助您充分利用 YOLOv5。请浏览 YOLOv5 <a href="https://docs.ultralytics.com/">文档</a> 了解详细信息，在 <a href="https://github.com/ultralytics/yolov5/issues/new/choose">GitHub</a> 上提交问题以获得支持，并加入我们的 <a href="https://discord.gg/n6cFeSPZdD">Discord</a> 社区进行问题和讨论！ | |
| 如需申请企业许可，请在 [Ultralytics Licensing](https://ultralytics.com/license) 处填写表格 | |
| <div align="center"> | |
| <a href="https://github.com/ultralytics" style="text-decoration:none;"> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" /> | |
| <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;"> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a> | |
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| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="" /></a> | |
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| <a href="https://www.tiktok.com/@ultralytics" style="text-decoration:none;"> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="" /></a> | |
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| <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;"> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" /> | |
| <a href="https://discord.gg/n6cFeSPZdD" style="text-decoration:none;"> | |
| <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="2%" alt="" /></a> | |
| </div> | |
| </div> | |
| ## <div align="center">YOLOv8 🚀 NEW</div> | |
| We are thrilled to announce the launch of Ultralytics YOLOv8 🚀, our NEW cutting-edge, state-of-the-art (SOTA) model | |
| released at **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**. | |
| YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of | |
| object detection, image segmentation and image classification tasks. | |
| See the [YOLOv8 Docs](https://docs.ultralytics.com) for details and get started with: | |
| ```commandline | |
| pip install ultralytics | |
| ``` | |
| <div align="center"> | |
| <a href="https://ultralytics.com/yolov8" target="_blank"> | |
| <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a> | |
| </div> | |
| ## <div align="center">文档</div> | |
| 有关训练、测试和部署的完整文档见[YOLOv5 文档](https://docs.ultralytics.com)。请参阅下面的快速入门示例。 | |
| <details open> | |
| <summary>安装</summary> | |
| 克隆 repo，并要求在 [**Python>=3.7.0**](https://www.python.org/) 环境中安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) ，且要求 [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/) 。 | |
| ```bash | |
| git clone https://github.com/ultralytics/yolov5 # clone | |
| cd yolov5 | |
| pip install -r requirements.txt # install | |
| ``` | |
| </details> | |
| <details> | |
| <summary>推理</summary> | |
| 使用 YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) 推理。最新 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 将自动的从 | |
| YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 | |
| ```python | |
| import torch | |
| # Model | |
| model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, custom | |
| # Images | |
| img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list | |
| # Inference | |
| results = model(img) | |
| # Results | |
| results.print() # or .show(), .save(), .crop(), .pandas(), etc. | |
| ``` | |
| </details> | |
| <details> | |
| <summary>使用 detect.py 推理</summary> | |
| `detect.py` 在各种来源上运行推理， [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从 | |
| 最新的YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载，并将结果保存到 `runs/detect` 。 | |
| ```bash | |
| python detect.py --weights yolov5s.pt --source 0 # webcam | |
| img.jpg # image | |
| vid.mp4 # video | |
| screen # screenshot | |
| path/ # directory | |
| list.txt # list of images | |
| list.streams # list of streams | |
| 'path/*.jpg' # glob | |
| 'https://youtu.be/Zgi9g1ksQHc' # YouTube | |
| 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream | |
| ``` | |
| </details> | |
| <details> | |
| <summary>训练</summary> | |
| 下面的命令重现 YOLOv5 在 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) 数据集上的结果。 | |
| 最新的 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data) | |
| 将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 | |
| YOLOv5n/s/m/l/x 在 V100 GPU 的训练时间为 1/2/4/6/8 天（ [多GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) 训练速度更快）。 | |
| 尽可能使用更大的 `--batch-size` ，或通过 `--batch-size -1` 实现 | |
| YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092) 。下方显示的 batchsize 适用于 V100-16GB。 | |
| ```bash | |
| python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 | |
| yolov5s 64 | |
| yolov5m 40 | |
| yolov5l 24 | |
| yolov5x 16 | |
| ``` | |
| <img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png"> | |
| </details> | |
| <details open> | |
| <summary>教程</summary> | |
| - [训练自定义数据](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data) 🚀 推荐 | |
| - [获得最佳训练结果的技巧](https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results) ☘️ | |
| - [多GPU训练](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) | |
| - [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) 🌟 新 | |
| - [TFLite，ONNX，CoreML，TensorRT导出](https://docs.ultralytics.com/yolov5/tutorials/model_export) 🚀 | |
| - [NVIDIA Jetson平台部署](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano) 🌟 新 | |
| - [测试时增强 (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation) | |
| - [模型集成](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling) | |
| - [模型剪枝/稀疏](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity) | |
| - [超参数进化](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution) | |
| - [冻结层的迁移学习](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers) | |
| - [架构概述](https://docs.ultralytics.com/yolov5/tutorials/architecture_description) 🌟 新 | |
| - [Roboflow用于数据集、标注和主动学习](https://docs.ultralytics.com/yolov5/tutorials/roboflow_datasets_integration) | |
| - [ClearML日志记录](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) 🌟 新 | |
| - [使用Neural Magic的Deepsparse的YOLOv5](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization) 🌟 新 | |
| - [Comet日志记录](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration) 🌟 新 | |
| </details> | |
| ## <div align="center">模块集成</div> | |
| <br> | |
| <a align="center" href="https://bit.ly/ultralytics_hub" target="_blank"> | |
| <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png"></a> | |
| <br> | |
| <br> | |
| <div align="center"> | |
| <a href="https://roboflow.com/?ref=ultralytics"> | |
| <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" /></a> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" /> | |
| <a href="https://cutt.ly/yolov5-readme-clearml"> | |
| <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" /></a> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" /> | |
| <a href="https://bit.ly/yolov5-readme-comet2"> | |
| <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" /></a> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" /> | |
| <a href="https://bit.ly/yolov5-neuralmagic"> | |
| <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" /></a> | |
| </div> | |
| | Roboflow | ClearML ⭐ 新 | Comet ⭐ 新 | Neural Magic ⭐ 新 | | |
| | :--------------------------------------------------------------------------------: | :-------------------------------------------------------------------------: | :--------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------: | | |
| | 将您的自定义数据集进行标注并直接导出到 YOLOv5 以进行训练 [Roboflow](https://roboflow.com/?ref=ultralytics) | 自动跟踪、可视化甚至远程训练 YOLOv5 [ClearML](https://cutt.ly/yolov5-readme-clearml)（开源！） | 永远免费，[Comet](https://bit.ly/yolov5-readme-comet2)可让您保存 YOLOv5 模型、恢复训练以及交互式可视化和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic)，运行 YOLOv5 推理的速度最高可提高6倍 | | |
| ## <div align="center">Ultralytics HUB</div> | |
| [Ultralytics HUB](https://bit.ly/ultralytics_hub) 是我们的⭐**新的**用于可视化数据集、训练 YOLOv5 🚀 模型并以无缝体验部署到现实世界的无代码解决方案。现在开始 **免费** 使用他！ | |
| <a align="center" href="https://bit.ly/ultralytics_hub" target="_blank"> | |
| <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a> | |
| ## <div align="center">为什么选择 YOLOv5</div> | |
| YOLOv5 超级容易上手，简单易学。我们优先考虑现实世界的结果。 | |
| <p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p> | |
| <details> | |
| <summary>YOLOv5-P5 640 图</summary> | |
| <p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p> | |
| </details> | |
| <details> | |
| <summary>图表笔记</summary> | |
| - **COCO AP val** 表示 mAP@0.5:0.95 指标，在 [COCO val2017](http://cocodataset.org) 数据集的 5000 张图像上测得， 图像包含 256 到 1536 各种推理大小。 | |
| - **显卡推理速度** 为在 [COCO val2017](http://cocodataset.org) 数据集上的平均推理时间，使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100实例，batchsize 为 32 。 | |
| - **EfficientDet** 数据来自 [google/automl](https://github.com/google/automl) ， batchsize 为32。 | |
| - **复现命令** 为 `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` | |
| </details> | |
| ### 预训练模型 | |
| | 模型 | 尺寸<br><sup>（像素） | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | 推理速度<br><sup>CPU b1<br>（ms） | 推理速度<br><sup>V100 b1<br>（ms） | 速度<br><sup>V100 b32<br>（ms） | 参数量<br><sup>(M) | FLOPs<br><sup>@640 (B) | | |
| | ---------------------------------------------------------------------------------------------- | --------------- | -------------------- | ----------------- | --------------------------- | ---------------------------- | --------------------------- | --------------- | ---------------------- | | |
| | [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** | | |
| | [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 | | |
| | [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 | | |
| | [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 | | |
| | [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 | | |
| | | | | | | | | | | | |
| | [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 | | |
| | [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 | | |
| | [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 | | |
| | [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 | | |
| | [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+[TTA] | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- | | |
| <details> | |
| <summary>笔记</summary> | |
| - 所有模型都使用默认配置，训练 300 epochs。n和s模型使用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) ，其他模型都使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml) 。 | |
| - \*\*mAP<sup>val</sup>\*\*在单模型单尺度上计算，数据集使用 [COCO val2017](http://cocodataset.org) 。<br>复现命令 `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` | |
| - **推理速度**在 COCO val 图像总体时间上进行平均得到，测试环境使用[AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/)实例。 NMS 时间 (大约 1 ms/img) 不包括在内。<br>复现命令 `python val.py --data coco.yaml --img 640 --task speed --batch 1` | |
| - **TTA** [测试时数据增强](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation) 包括反射和尺度变换。<br>复现命令 `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` | |
| </details> | |
| ## <div align="center">实例分割模型 ⭐ 新</div> | |
| 我们新的 YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) 实例分割模型是世界上最快和最准确的模型，击败所有当前 [SOTA 基准](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco)。我们使它非常易于训练、验证和部署。更多细节请查看 [发行说明](https://github.com/ultralytics/yolov5/releases/v7.0) 或访问我们的 [YOLOv5 分割 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) 以快速入门。 | |
| <details> | |
| <summary>实例分割模型列表</summary> | |
| <br> | |
| <div align="center"> | |
| <a align="center" href="https://ultralytics.com/yolov5" target="_blank"> | |
| <img width="800" src="https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png"></a> | |
| </div> | |
| 我们使用 A100 GPU 在 COCO 上以 640 图像大小训练了 300 epochs 得到 YOLOv5 分割模型。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于再现，我们在 Google [Colab Pro](https://colab.research.google.com/signup) 上进行了所有速度测试。 | |
| | 模型 | 尺寸<br><sup>（像素） | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 训练时长<br><sup>300 epochs<br>A100 GPU（小时） | 推理速度<br><sup>ONNX CPU<br>（ms） | 推理速度<br><sup>TRT A100<br>（ms） | 参数量<br><sup>(M) | FLOPs<br><sup>@640 (B) | | |
| | ------------------------------------------------------------------------------------------ | --------------- | -------------------- | --------------------- | --------------------------------------- | ----------------------------- | ----------------------------- | --------------- | ---------------------- | | |
| | [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** | | |
| | [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 | | |
| | [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 | | |
| | [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 | | |
| | [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 | | |
| - 所有模型使用 SGD 优化器训练， 都使用 `lr0=0.01` 和 `weight_decay=5e-5` 参数， 图像大小为 640 。<br>训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5_v70_official | |
| - **准确性**结果都在 COCO 数据集上，使用单模型单尺度测试得到。<br>复现命令 `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt` | |
| - **推理速度**是使用 100 张图像推理时间进行平均得到，测试环境使用 [Colab Pro](https://colab.research.google.com/signup) 上 A100 高 RAM 实例。结果仅表示推理速度（NMS 每张图像增加约 1 毫秒）。<br>复现命令 `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1` | |
| - **模型转换**到 FP32 的 ONNX 和 FP16 的 TensorRT 脚本为 `export.py`.<br>运行命令 `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half` | |
| </details> | |
| <details> | |
| <summary>分割模型使用示例 <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary> | |
| ### 训练 | |
| YOLOv5分割训练支持自动下载 COCO128-seg 分割数据集，用户仅需在启动指令中包含 `--data coco128-seg.yaml` 参数。 若要手动下载，使用命令 `bash data/scripts/get_coco.sh --train --val --segments`， 在下载完毕后，使用命令 `python train.py --data coco.yaml` 开启训练。 | |
| ```bash | |
| # 单 GPU | |
| python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 | |
| # 多 GPU， DDP 模式 | |
| python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3 | |
| ``` | |
| ### 验证 | |
| 在 COCO 数据集上验证 YOLOv5s-seg mask mAP： | |
| ```bash | |
| bash data/scripts/get_coco.sh --val --segments # 下载 COCO val segments 数据集 (780MB, 5000 images) | |
| python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # 验证 | |
| ``` | |
| ### 预测 | |
| 使用预训练的 YOLOv5m-seg.pt 来预测 bus.jpg： | |
| ```bash | |
| python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg | |
| ``` | |
| ```python | |
| model = torch.hub.load( | |
| "ultralytics/yolov5", "custom", "yolov5m-seg.pt" | |
| ) # 从load from PyTorch Hub 加载模型 (WARNING: 推理暂未支持) | |
| ``` | |
| |  |  | | |
| | ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | | |
| ### 模型导出 | |
| 将 YOLOv5s-seg 模型导出到 ONNX 和 TensorRT： | |
| ```bash | |
| python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0 | |
| ``` | |
| </details> | |
| ## <div align="center">分类网络 ⭐ 新</div> | |
| YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) 带来对分类模型训练、验证和部署的支持！详情请查看 [发行说明](https://github.com/ultralytics/yolov5/releases/v6.2) 或访问我们的 [YOLOv5 分类 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) 以快速入门。 | |
| <details> | |
| <summary>分类网络模型</summary> | |
| <br> | |
| 我们使用 4xA100 实例在 ImageNet 上训练了 90 个 epochs 得到 YOLOv5-cls 分类模型，我们训练了 ResNet 和 EfficientNet 模型以及相同的默认训练设置以进行比较。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于重现，我们在 Google 上进行了所有速度测试 [Colab Pro](https://colab.research.google.com/signup) 。 | |
| | 模型 | 尺寸<br><sup>（像素） | acc<br><sup>top1 | acc<br><sup>top5 | 训练时长<br><sup>90 epochs<br>4xA100（小时） | 推理速度<br><sup>ONNX CPU<br>（ms） | 推理速度<br><sup>TensorRT V100<br>（ms） | 参数<br><sup>(M) | FLOPs<br><sup>@640 (B) | | |
| | -------------------------------------------------------------------------------------------------- | --------------- | ---------------- | ---------------- | ------------------------------------ | ----------------------------- | ---------------------------------- | -------------- | ---------------------- | | |
| | [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** | | |
| | [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 | | |
| | [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 | | |
| | [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 | | |
| | [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 | | |
| | | | | | | | | | | | |
| | [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 | | |
| | [Resnetzch](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 | | |
| | [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 | | |
| | [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 | | |
| | | | | | | | | | | | |
| | [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 | | |
| | [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 | | |
| | [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 | | |
| | [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 | | |
| <details> | |
| <summary>Table Notes (点击以展开)</summary> | |
| - 所有模型都使用 SGD 优化器训练 90 个 epochs，都使用 `lr0=0.001` 和 `weight_decay=5e-5` 参数， 图像大小为 224 ，且都使用默认设置。<br>训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2 | |
| - **准确性**都在单模型单尺度上计算，数据集使用 [ImageNet-1k](https://www.image-net.org/index.php) 。<br>复现命令 `python classify/val.py --data ../datasets/imagenet --img 224` | |
| - **推理速度**是使用 100 个推理图像进行平均得到，测试环境使用谷歌 [Colab Pro](https://colab.research.google.com/signup) V100 高 RAM 实例。<br>复现命令 `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1` | |
| - **模型导出**到 FP32 的 ONNX 和 FP16 的 TensorRT 使用 `export.py` 。<br>复现命令 `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224` | |
| </details> | |
| </details> | |
| <details> | |
| <summary>分类训练示例 <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary> | |
| ### 训练 | |
| YOLOv5 分类训练支持自动下载 MNIST、Fashion-MNIST、CIFAR10、CIFAR100、Imagenette、Imagewoof 和 ImageNet 数据集，命令中使用 `--data` 即可。 MNIST 示例 `--data mnist` 。 | |
| ```bash | |
| # 单 GPU | |
| python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128 | |
| # 多 GPU， DDP 模式 | |
| python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3 | |
| ``` | |
| ### 验证 | |
| 在 ImageNet-1k 数据集上验证 YOLOv5m-cls 的准确性： | |
| ```bash | |
| bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) | |
| python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate | |
| ``` | |
| ### 预测 | |
| 使用预训练的 YOLOv5s-cls.pt 来预测 bus.jpg： | |
| ```bash | |
| python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg | |
| ``` | |
| ```python | |
| model = torch.hub.load( | |
| "ultralytics/yolov5", "custom", "yolov5s-cls.pt" | |
| ) # load from PyTorch Hub | |
| ``` | |
| ### 模型导出 | |
| 将一组经过训练的 YOLOv5s-cls、ResNet 和 EfficientNet 模型导出到 ONNX 和 TensorRT： | |
| ```bash | |
| python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224 | |
| ``` | |
| </details> | |
| ## <div align="center">环境</div> | |
| 使用下面我们经过验证的环境，在几秒钟内开始使用 YOLOv5 。单击下面的图标了解详细信息。 | |
| <div align="center"> | |
| <a href="https://bit.ly/yolov5-paperspace-notebook"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gradient.png" width="10%" /></a> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" /> | |
| <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="10%" /></a> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" /> | |
| <a href="https://www.kaggle.com/ultralytics/yolov5"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="10%" /></a> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" /> | |
| <a href="https://hub.docker.com/r/ultralytics/yolov5"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="10%" /></a> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" /> | |
| <a href="https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="10%" /></a> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" /> | |
| <a href="https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="10%" /></a> | |
| </div> | |
| ## <div align="center">贡献</div> | |
| 我们喜欢您的意见或建议！我们希望尽可能简单和透明地为 YOLOv5 做出贡献。请看我们的 [投稿指南](https://docs.ultralytics.com/help/contributing/)，并填写 [YOLOv5调查](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 向我们发送您的体验反馈。感谢我们所有的贡献者！ | |
| <!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 --> | |
| <a href="https://github.com/ultralytics/yolov5/graphs/contributors"> | |
| <img src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" /></a> | |
| ## <div align="center">License</div> | |
| YOLOv5 在两种不同的 License 下可用： | |
| - **AGPL-3.0 License**： 查看 [License](https://github.com/ultralytics/yolov5/blob/master/LICENSE) 文件的详细信息。 | |
| - **企业License**：在没有 AGPL-3.0 开源要求的情况下为商业产品开发提供更大的灵活性。典型用例是将 Ultralytics 软件和 AI 模型嵌入到商业产品和应用程序中。在以下位置申请企业许可证 [Ultralytics 许可](https://ultralytics.com/license) 。 | |
| ## <div align="center">联系我们</div> | |
| 对于 YOLOv5 的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues)，并加入我们的 [Discord](https://discord.gg/n6cFeSPZdD) 社区进行问题和讨论！ | |
| <br> | |
| <div align="center"> | |
| <a href="https://github.com/ultralytics" style="text-decoration:none;"> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="" /></a> | |
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| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="" /></a> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" /> | |
| <a href="https://www.tiktok.com/@ultralytics" style="text-decoration:none;"> | |
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| <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;"> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="" /></a> | |
| <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" /> | |
| <a href="https://discord.gg/n6cFeSPZdD" style="text-decoration:none;"> | |
| <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="3%" alt="" /></a> | |
| </div> | |
| [tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation | |